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Data Science: Education Policy and Administration

Published onNov 17, 2023
Data Science: Education Policy and Administration

This panel provides an analysis of data science curriculum policy and administration at four-year and two-year higher education institutions and an application of data science using generative AI for improved educational equity. McNeil presents an overview of data science curriculum policy at four-year institutions being shaped by social and market forces using the theories of academic capitalism and isomorphism. Future analyses will critically evaluate what “good” data science education and administration are and look at big normative questions surrounding data science. Saidi advocates for expanding data science programs at community colleges in order to bolster student enrollment and diversity. Different entry points, accessible routes through K-12 pathways, articulations with four-year institutions, and industry and government partnerships will be discussed. Sun applies human-centered data science methods to analyze policy stakeholders’ statements on their lived experiences and their options about the assets and gaps in state policies of advancing racial and income equity in public K-12 education systems. The conclusion is reached that a computer-human sequential approach offers a better integration of the advantages of both computer and human coding. Together, the three talks give a critical evaluation of data science education and administration and a usage of data science by public managers for enhanced service delivery. 


  • Torbet McNeil, Ph.D. Candidate; Graduate Research Associate, Institute for Computation and Data-Enabled Insight, University of Arizona

  • Rachel Saidi, Professor of Math, Statistics, and Data Science; Data Science Program Director, Montgomery College

  • Min Sun, Professor, University of Washington Seattle

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